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As a promising ultrawide bandgap oxide semiconductor material in the spinel family, magnesium gallate (MgGa2O4) exhibits great potential applications in power electronics, transparent electronics, and deep ultraviolet optoelectronics. However, few studies reveal its photoluminescence (PL) properties. In this work, MgGa2O4 films were grown by using oxygen plasma assisted molecular beam epitaxy. The bandgap of MgGa2O4 spinel films is determined to be around 5.4–5.5 eV, and all samples have transmittance over 90% in the visible spectral range. X-ray diffraction patterns confirmed that the spinel films were grown highly along ⟨111⟩ oriented. Power and temperature dependent PL studies were investigated. Optical transitions involving self-trapped hole, oxygen vacancy deep donor, and magnesium atom on gallium site deep acceptor levels were revealed. 

We present a hierarchical control approach for maneuvering an autonomous vehicle (AV) in tightly-constrained environments where other moving AVs and/or human driven vehicles are present. A two-level hierarchy is proposed: a high-level data-driven strategy predictor and a lower-level model-based feedback controller. The strategy predictor maps an encoding of a dynamic environment to a set of high-level strategies via a neural network. Depending on the selected strategy, a set of time-varying hyperplanes in the AV’s position space is generated online and the corresponding halfspace constraints are included in a lower-level model-based receding horizon controller. These strategy-dependent constraints drive the vehicle towards areas where it is likely to remain feasible. Moreover, the predicted strategy also informs switching between a discrete set of policies, which allows for more conservative behavior when prediction confidence is low. We demonstrate the effectiveness of the proposed data-driven hierarchical control framework in a two-car collision avoidance scenario through simulations and experiments on a 1/10 scale autonomous car platform where the strategy-guided approach outperforms a model predictive control baseline in both cases.